Machine Learning for Computational Healthcare

Machine learning is a powerful tool for achieving computational intelligence. However, its success crucially relies on having a good feature representation of the data—having poor representations can severely limit the performance of learning algorithms. In recent years, unsupervised feature learning and deep learning have emerged as highly promising methods for learning useful feature representations from data. In this project, we will investigate (1) learning feature representations from medical data and (2) making predictions in supervised and/or semi-supervised settings. We expect that this project will result in powerful techniques which can learn important high-level abstractions (e.g., computational biomarkers) from data and improve the performance of computer-aided diagnosis and risk prediction.